Microsoft PL-300-KR exam dumps : Microsoft Power BI Data Analyst (PL-300 Korean Version)

  • Exam Code: PL-300-KR
  • Exam Name: Microsoft Power BI Data Analyst (PL-300 Korean Version)
  • Updated: Aug 31, 2026     Q & A: 472 Questions and Answers

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What is the salary of the Microsoft Power BI Data Analyst Certified professional

The Average salary of different countries of Microsoft Power BI Data Analyst certified professionals:

  • Canada; CAD 70,000

  • United States; USD 90,000

  • United Kingdom; POUNDS 60,000

  • Australia; AUD 75,000

  • India; INR 500,000

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/pl-300

Understanding Microsoft Power BI Data Analyst Certification Exam Topics

Microsoft Power BI Data Analyst Certification Exam Topics are below:

  • Visualize and analyze the data (25-30%)
  • Model the data (30-35%)
  • Prepare the data (15-20%)
  • Deploy and maintain assets (20-25%)

For more info visit:

Microsoft Power BI Data Analyst Certification Exam Reference

Microsoft PL-300 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Prepare the data25-30%- Load and configure data
  • 1. Define table structures and properties
    • 2. Configure data refresh settings
      • 3. Integrate data from multiple sources
        - Get or connect to data
        • 1. Choose storage modes: Import, DirectQuery, DirectLake
          • 2. Identify and connect to data sources or shared semantic models
            • 3. Create and modify parameters
              • 4. Configure data source settings, credentials, and privacy levels
                - Profile and clean data
                • 1. Resolve inconsistencies, null values, and errors
                  • 2. Manage data types, columns, and rows
                    • 3. Transform data using Power Query Editor
                      • 4. Evaluate data statistics and column properties
                        Topic 2: Manage and secure Power BI15-20%- Implement security and governance
                        • 1. Configure row-level security (RLS) and object-level security
                          • 2. Apply data sensitivity labels and protection
                            • 3. Manage permissions and access
                              - Deploy and distribute content
                              • 1. Embed content in applications or websites
                                • 2. Share reports, dashboards, and apps
                                  • 3. Monitor usage and performance
                                    - Manage workspaces and assets
                                    • 1. Publish, update, and organize reports/datasets
                                      • 2. Create and configure workspaces
                                        • 3. Manage data refresh and scheduled updates
                                          Topic 3: Model the data25-30%- Optimize model performance
                                          • 1. Optimize relationships and calculations
                                            • 2. Use aggregations and composite models
                                              • 3. Reduce cardinality and data size
                                                - Design data models
                                                • 1. Create relationships: cardinality, cross-filter direction, security
                                                  • 2. Create common date tables
                                                    • 3. Implement role-playing dimensions
                                                      • 4. Define tables, columns, and hierarchies
                                                        - Create model calculations
                                                        • 1. Use CALCULATE and filter context
                                                          • 2. Implement aggregation and time intelligence
                                                            • 3. Use DAX: calculated columns, calculated tables, measures
                                                              Topic 4: Visualize and analyze data25-30%- Create reports and dashboards
                                                              • 1. Choose appropriate visualizations
                                                                • 2. Apply filters, slicers, and drill-through
                                                                  • 3. Design report layout, formatting, and interactivity
                                                                    • 4. Create dashboards and pinned elements
                                                                      - Enhance reports with analytics
                                                                      • 1. Apply conditional formatting and custom visuals
                                                                        • 2. Create paginated reports
                                                                          • 3. Use AI visuals, forecasts, and key influencers
                                                                            - Perform data analysis
                                                                            • 1. Create and manage bookmarks and tooltips
                                                                              • 2. Implement custom visuals and R/Python scripts
                                                                                • 3. Interpret results and identify trends

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